Cross-Modal Entailment Classification via Feature Segmentation
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Solution Overview
Problem
Existing methods for determining entailment between inputs of different modalities, such as text and images, are time-consuming and laborious, especially in critical applications like court cross-examination, due to the spread of misinformation and disinformation, necessitating a machine-learnable approach for generalized cross-modal entailment tasks.
Innovation Solution
A computer-implemented method that extracts features from input premises and hypotheses, derives intra-modal relevant information, attaches cross-modal relevant information, and classifies the relationship between inputs using a hardware processor, employing techniques like self-attention and cross-modal interactions to form a cross-modal representation for entailment classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual fact-checking is performed to ensure accurate entailment classification, then reliability is improved, but productivity deteriorates due to time-consuming and laborious processes
Solution Approach 1:
The patent replaces manual mechanical fact-checking with an automated neural network system that processes cross-modal inputs (text and images) through learned representations. The system substitutes human analysts with machine learning models that can automatically classify entailment relationships, maintaining reliability while dramatically improving productivity by processing multiple inputs in parallel without human intervention
Solution Approach 2:
The neural network system performs self-service by automatically learning cross-modal representations and making entailment classifications without requiring manual annotation or human intervention during operation. The model trains on labeled data and then autonomously processes new inputs, enabling the system to serve itself and eliminate the need for continuous human oversight while maintaining consistent classification quality
2Measurement precision
If comprehensive feature extraction from entire premise is performed to improve measurement precision, then device complexity increases due to processing large volumes of data
Solution Approach 1:
The patent segments the premise into distinct regions of interest rather than processing the entire premise uniformly. The system identifies and extracts features from specific relevant regions within the premise, dividing the complex processing task into manageable segments. This segmentation maintains measurement precision by focusing on critical areas while reducing device complexity by avoiding unnecessary processing of irrelevant regions
Solution Approach 2:
The system extracts only the essential features and regions of interest from the premise, taking out the relevant information needed for entailment classification while discarding redundant data. This selective extraction process maintains measurement precision by capturing critical features while significantly reducing computational complexity by eliminating unnecessary processing of irrelevant information
Data Source
AI summary
A method is provided for determining entailment between an input premise and an input hypothesis of different modalities. The method includes extracting features from the input hypothesis and an entirety of and regions of interest in the input premise. The method further includes deriving intra-modal relevant information while suppressing intra-modal irrelevant information, based on intra-modal interactions between elementary ones of the features of the input hypothesis and between elementary ones of the features of the input premise. The method also includes attaching cross-modal relevant information to the features from the input premise to the features from the input hypothesis to form a cross-modal representation, based on cross-modal interactions between pairs of different elementary features from different modalities. The method additionally includes classifying a relationship between the input premise and the input hypothesis using a label selected from the group consisting of entailment, neutral, and contradiction based on the cross-modal representation.